Senior Credit Risk Analyst - Rogers, Toronto

AI Summary

Rogers Bank in Toronto is seeking a Senior Credit Risk Analyst. This role involves developing and implementing statistical and machine learning models to support critical business decisions, including credit risk, marketing, and fraud. Key responsibilities include assisting with IFRS 9 expected credit loss model updates and collaborating with various teams. Candidates should possess a Bachelor's degree in a quantitative field and at least two years of experience with statistical programs like SAS or Python, along with strong SQL skills.

About the Opportunity at Rogers Bank

Rogers Bank, a dynamic subsidiary of Canada's leading wireless, cable, and media company, Rogers Communications, is actively expanding its financial services team in Toronto. This presents an exceptional opportunity for a Senior Credit Risk Analyst to play a pivotal role in developing and implementing innovative financial solutions. The successful candidate will contribute significantly to the bank's mission by optimizing risk and reward decisions through advanced data analysis. This role is crucial for a growing financial institution committed to leveraging cutting-edge technology to offer unique cash-back benefits and financing options for Rogers purchases. The team values individuals passionate about interpreting complex data to inform strategic business decisions and offers a challenging yet rewarding environment focused on both financial and business acumen. Joining Rogers Bank means becoming part of a forward-thinking organization that invests in its people and fosters a culture of growth and development.

Key Responsibilities and Required Expertise

As a Senior Credit Risk Analyst at Rogers Bank, your primary responsibilities will involve the development and implementation of sophisticated statistical and machine learning models designed to support critical business decisions across various domains, including credit risk, marketing, fraud prevention, and collections. A key aspect of this role is assisting with the ongoing updates and maintenance of the IFRS 9 expected credit loss (ECL) model, ensuring compliance and accuracy. You will engage in collaborative efforts with cross-functional teams within the bank to gather essential requirements and data for model development, followed by rigorous testing and validation to guarantee their accuracy and reliability. Meticulous documentation of all statistical models, detailing assumptions, limitations, methodology, and interpretation of results, is paramount. Continuous monitoring and refinement of these models will be essential to maintain their relevance and effectiveness in a dynamic financial landscape. Furthermore, you will be expected to develop compelling dashboards and visualizations to communicate model insights, presenting findings and strategic recommendations to stakeholders, including senior management. The role also involves conducting in-depth statistical analysis to identify predictive factors in customer behaviour, such as payment patterns, purchase activity, and delinquency trends, and to quantify the impact of changes to risk strategy and process on key revenue and loss metrics. A comprehensive understanding of data flows and processes from various business areas is vital to support robust reporting, analysis, and modelling efforts. Candidates must possess a Bachelor’s degree in Mathematics, Statistics, Business, Finance, Economics, or an equivalent quantitative field, coupled with at least two years of experience using statistical programs like SAS, Python, or Databricks. Proficiency in database querying tools such as SQL and advanced Microsoft Excel skills are also required, with experience in data visualization tools like PowerBI being a strong asset. Experience within a finance or credit card environment is preferred, as is knowledge of IFRS 9 ECL calculation.